Turn Your AI Assistant Into a Prometheus Expert

Stop context-switching between your AI chat and Prometheus dashboards. This MCP server gives your AI assistant direct access to your Prometheus metrics, so you can troubleshoot performance issues, analyze trends, and investigate incidents without leaving your conversation.

Skip the PromQL Learning Curve

Your AI assistant can now run complex PromQL queries for you. Ask it to "find services with high error rates in the last hour" or "show me memory usage trends for my backend pods" and get actual data instead of generic advice.

Instead of manually crafting queries like:

rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m]) > 0.01

Just tell your assistant: "Find services with error rates above 1% in the last 5 minutes."

Real Monitoring Intelligence

The server exposes five focused tools that transform how you interact with your metrics:

• execute_query: Run instant PromQL queries for current state analysis
• execute_range_query: Get time-series data with custom intervals for trend analysis
• list_metrics: Discover available metrics when you're not sure what's instrumented
• get_metric_metadata: Understand metric types and help text without digging through documentation
• get_targets: Check scrape target health and configuration

Your assistant can now correlate metrics across services, identify patterns in your infrastructure, and suggest optimizations based on actual data patterns.

Production-Ready Integration

Built for real environments with authentication support (basic auth and bearer tokens), multi-tenant setups (Cortex/Mimir/Thanos), and configurable tool sets to avoid cluttering your AI's context window.

The Docker container approach means zero local Python dependencies:

{
  "mcpServers": {
    "prometheus": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "PROMETHEUS_URL",
        "ghcr.io/pab1it0/prometheus-mcp-server:latest"
      ],
      "env": {
        "PROMETHEUS_URL": "http://your-prometheus:9090"
      }
    }
  }
}

Transform Your Incident Response

During outages, your assistant can immediately pull relevant metrics, correlate across services, and help identify root causes while you focus on fixes rather than query syntax.

Ask questions like:

• "What changed in CPU usage before the alerts started?"
• "Show me request latency patterns for the payment service"
• "Which pods are consuming the most memory right now?"

And get actionable data instead of having to remember PromQL syntax under pressure.

Your monitoring data becomes conversational, making your AI assistant as knowledgeable about your infrastructure as your most experienced SRE.